{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RF to B-Mode Generation\n",
    "\n",
    "Ultrasound imaging employs acoustic pressure waves to gather information describing changes in tissue permeability. A transducer emits an ultrasound beam at discrete time intervals and translates the pressure wave echo into radio frequency waves. These electromagnetic waves are then readable with an analog-to-digital converter for machine input.\n",
    "\n",
    "The following example notebook showcases how the ITK Ultrasound module may be used to generate a human-readable brightness-mode (B-Mode) image from RF-Mode input collected from a transducer. The input image `MouseLiverRF.mha` describes one ultrasound capture representing axial, lateral, and time dimensions. The `itk.b_mode_image_filter` function performs envelope detection on the RF signal along the axial direction and applies a logarithmic intensity transform to the image output for visibility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install notebook dependencies\n",
    "import sys\n",
    "#!{sys.executable} -m pip install itk itk-ultrasound numpy matplotlib itkwidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itk\n",
    "from matplotlib import pyplot as plt\n",
    "from itkwidgets import view, compare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read RF Image\n",
    "\n",
    "The example RF image represents raw transducer output describing a mouse liver sample. Taken alone, the RF sample fails to deliver good insight into the mouse liver image. We must perform envelope detection and brightness filtering on the image in order to generate a BMode image for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "itkSize3 ([1536, 128, 4])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "830e540756e74c68972c7f0237c67840",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageF3; pro…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SAMPLING_FREQUENCY = 40e6 # Hz\n",
    "SAMPLING_PERIOD = SAMPLING_FREQUENCY ** -1\n",
    "\n",
    "# Image dimensions: [axial, lateral, frame]\n",
    "rf_image = itk.imread('Input/MouseLiverRF.mha', itk.F)\n",
    "print(itk.size(rf_image))\n",
    "view(rf_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RF image represents transducer results over a certain time period with regards to the axial direction (along the beam) and the lateral direction (normal to the beam). Data along the axial direction describes how the ultrasound wave echo propagated through the image medium. Here we use `matplotlib` to visualize the waveform at a fixed lateral position at T=0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_beam_waveform(image, lateral_idx=0, time_idx=0):\n",
    "    arr = itk.array_view_from_image(image)\n",
    "    x_labels = [idx * SAMPLING_PERIOD for idx in range(0,arr.shape[2])]\n",
    "    plt.plot(x_labels,arr[time_idx,lateral_idx,:])\n",
    "    plt.xlabel('Sampling Time ($\\mu$s)')\n",
    "    plt.ylabel('Response')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize one RF waveform\n",
    "visualize_beam_waveform(rf_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform Envelope Detection\n",
    "\n",
    "To understand how the pressure wave echo travelled through the imaging medium we apply amplitude demodulation through envelope detection. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note that dimension in direction of analytic signal filter must be\n",
    "# a multiple of 2,3,5 for the FFT to be valid\n",
    "padded_image = itk.fft_pad_image_filter(rf_image)\n",
    "complex_image = itk.analytic_signal_image_filter(rf_image,direction=0)\n",
    "modulus_image = itk.complex_to_modulus_image_filter(complex_image)\n",
    "\n",
    "visualize_beam_waveform(modulus_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00c2ca32ed8b46468154ae3222d06b0c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageF3; pro…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view(modulus_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply Logarithmic Transform\n",
    "\n",
    "To get data on a more useful scale for analysis we apply a log10 transform over the intensity image. Other transforms such as smoothing and clamping may also be applied to help translate the intensity image into a human-interpretable format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_image = itk.log10_image_filter(modulus_image)\n",
    "\n",
    "visualize_beam_waveform(log_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "355e1eed38da4bd8b628c54dd4cde0db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageF3; pro…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view(log_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `BModeImageFilter` for RF-to-BMode image generation\n",
    "\n",
    "`itk.BModeImageFilter` combines envelope detection and logarithmic transformation to produce a straightforward pipeline for BMode image generation. Note that `MouseLiverB.mha` is produced using a similar method but with additional transformations for visibility and is intended only for general comparison here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "bmode_image = itk.imread('Input/MouseLiverB.mha', itk.F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6868aa6d75ad46c2b2416c182f5bdaa6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "AppLayout(children=(HBox(children=(Label(value='Link:'), Checkbox(value=False, description='cmap'), Checkbox(v…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered_image = itk.b_mode_image_filter(rf_image,direction=1)\n",
    "compare(filtered_image, bmode_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Beamline Over Time\n",
    "\n",
    "We can plot pixel values along the axial direction over time to investigate how the BMode filter transforms data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BMode Image (calculated)\n",
    "visualize_beam_waveform(filtered_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/W+R2IqLbiWgvEb1BROfN5uDLRbdlClk0hiUQDlBemV8/lC+vLhqhIMkJVRZ3hmEqQymR+88BvM+z7VYAzwghVgN4Rj4HgGsArJb/bgLwo5kZ5syiR+5Jw1/cTcuJ3L0LlfyIqMg97UTubMswDFNpiiqXEOI5AMc9m68DcLd8fDeAD2nb/0U4vAygmYgWzdBYp8ShgQmsuPVRvPhWf95rE5ms554y/AXYtB3P3VsszI+wzIVXdwHKlpmP5QeOjaSw4tZH8ezOY5UeCsMw06Bcz71DCNEtH/cA6JCPlwA4rO13RG6bc17eNwAAePD1rrzXkiVF7k62TKSERUxezz0conmbLbO7dwwA8OP1b1V4JAzDTIdpT6gKJ0dwyipGRDcR0QYi2tDX1zfdYeShWt/51YTRbZlUIXG3nTz3Jc11RT8rz3MPBBAJBedl5B6VhdKOywVZDMPMT8oV915lt8if6h6+C8Aybb+lclseQog7hBDrhBDr2tvbyxxGYVR3pIDPGerReqHI3bCcFaor2hJFP8vruTsrVGleNutQdzWDEyzuDDOfKVfcHwZwo3x8I4CHtO03yKyZiwAMa/bNnKIWHfkV/NJtmfRkkXswgHpZm30ylC3jLmIKEcLB+Tmhqi52HLkzzPymqHIR0b0ALgPQRkRHAHwNwD8AeICIPgvgIICPyd0fA3AtgL0AJgB8ZhbGXBIqcvebEC0lW8aQnnspqAnVCc8ipvloyyibqpRyCwzDVC9FxV0IcX2Bl97js68AcPN0BzUTqA53vpG7UTxbxrKF26jj2b+6zK0S6UfWc3eOGwo4ee7pAseuZiZKKM3AMEz1U7MrVO1JIvdkxkJjLOQ+9kMVDgOAlW0JrO5oKPhZWc/dQjhIICI0xMI5q19//sJ+HBmcKO9k5pBS6u4wDFP91Ky4u9kyPuKeNm00xZ3+pylz8gnVUlCee9Kw3MdNdWGMZywYlo2+0TS+/sh2/Om/bJzyecw1uk1lsTfDMPOWmhV3ZYn4NVDKmDbqo1LcC0XudjZyL4a+ijUr7s6dwUjScCcnVUngakaP3OfjhDDDMA41K+4qIvcr+JWxbERDAcTCAaQKpCuqqpCloEf4rrjLO4PhpIF+KeqltOqrNHrknp6HqZwMwzjUrLiryN0v1zxtOuJeFw4W9tzt0rNliMgV+Lb6CADHlgG84j61c6gEurhz5M4w85eaFXcVufsJVMa0EQkFEAsHC69QlYXDSkVF7B2NMQBZcR9JmegbleI+TXXPmDb+7pHtOROze3pH8e3f7XAnkKeLfrGbj4uwGIZxqF1xNyYXdzdyL5Tnbpc+oQro4h4F4I3cMwXHMhV29YziZy/sx6XfeRaAs1Drqu8/h5+s34ejw8lpHVvB4s4wtUHNiruyZQwrP6LNWE7kHg0HkTJsDI5n8jJD9FTIUlBBeaeM3Btjjrh/4d5N6BpyhHdwwijYrq8UxtK5HaR0T3w46d90ZKqwLcMwtUHNiruK3P1WiWZMG5FgAHXhAPpGUzj3m0/hO4/vzNlnKhOqQFZcFypbRk6oAsDh4xPu545PI4983CvuxiyIe4YnVBmmFqhdcVeeu49A6Z5774jjhz+0Obe+2VQmVIHscv3FzY64R0NBfOE9qwHk1mmZjgjrkbtli5wc/ZFk8b6wpTCSMtxFWRy5M8z8pWbFPWvL+Ii7tGXqwkEMjDvi7p2PVIXDpsqp2krW809qAZAr7iNS3O98bt+UG2Lo4p4x7ZzJ4JEZityHkwbaG6LuZzAMMz+pWXFXom76ZJE4tkwQsXDQ9eT1bBMhhNtDdaro9d9VRUldlIeTBixb4FuP7cBnfv7alI6t2zJp08rxx2fKlhlOGmhT4s6RO8PMW2pW3JWo++e5W64to7C0iU41uVpO5E7aQiVVvwYAFiSc/PfhpIEDA+NTPi6QK+4pw84pejYT4m5YNiYyFtplrj7bMgwzfylerHyeogTaK1C27UTlEblCVd+uMF1xLz1yf/JL78prpl2viXt7QxQD4xkMJw3s6B5xtz+78xguWNmKRAl140c9kXtqhiN3dQy2ZRhm/lOzkbstlLjn2jLKalB57go9Q9EV9ynYMqd2NGClp2uT3uijrd4RzJGkgQP92cj9Mz9/Dd94ZFtJn+GN3L22TPdwEnc9v7/sdMu7nt8PAGivV7YMFw5jmPnKCRe56+JeyJZRtdunkufuRyISApFz4WhNREDkiHvaMyaVB1+M8bSepmi5XaQC5Pj6X7xvM17ZfxyXndaOk9vrpzzeH/2H0xS7jSN3hpn31GzkrsTdOymoBCsSCqAukhV3U4tSVbQ/lRWqfgQChPqIc/2MhgJojIUxnDQwnjbRoFk2rYloScfLtWWynntrIoqJjOnaNENl9D/VF3EtkONhz51h5i+1K+4iP3JPmxa2dg0DcBpsREPZ089Y2dRC05aRexkTql6U7x4JBdAcD2MoaWA8baFZW+Sk2y0HB8YL+ufjaRMROeaUkfXcWxNhpwGJLHnQNzp1cdctHlX8jCN3hpm/1Ky4S32GYWYj0p+s34f/+s9O+qE3cgeyLeZUFD8Vz70QynePhAJoTUQwMJbBWNpEIhLCS1+5AictiLtVI4UQePd3/wOf/OnLvsdKZiy0yItCWvPcW+IRTGQs927g2GhqyuNULQL/4G2LsXZJEwAWd4aZz9SsuCubQYkWkJtvHgkFEAvliruKoFW0781+KQc9cm+rj6J/LI2xlGPLLGqqw7qTWtEvq0aqxU5bu0Z8j5UyLTTXRdzHypZZUO+Iu5pD6BmeurirsgPvPrXdtaM4z51h5i81K+4q42U0bbpCryo1Ao5w6xOqADAuLwTWJP1Xp0qDLCAWDWbFfTxjuqmPbQ0R9I2lIYQomv+eylhuzZq0kbWRmuMRJA3LXZXbM1KGuMtjxSNBt/wAR+4MM3+pWXFXqZBCZJfm6/67YWVtDWVnqGyUmZpQBYAGKeLRcBDtDVEcH89gJGm44t4YC8OwBNKmjf39E3KbfxJT0rDQLC9QaVl+IBIKIBFxmo6ou5SJ9NSLkylLqi4SdJuPcOTOMPOXmhV3yxauSA55xP17f/Q2XL220/WvP3D2YgBZW8adUJ1mKiSgee7BANrrI7AFcHgw6WbRqLuHtGHjkKwe2ajdYeikDBstcWnLyAnVunAQdZEQkoblVpwsJ8tF2TJxOZ7JulQxDFP91Ky427ZwFw4NytTAjGmjLhzEH56/FOFgAFecvhCPfuFSfPqikwBk/XkVuU9lhWohvJ474Fx4VOSuVsmmTAvDcpwTPqIqhHAi97geuTtNR+JyYnhQevZGGV2Z9MgdAOKREIs7w8xjalbcTVtggUzpu+W+TbBk2QHdaiEirF3c5EbXypYxZ3BCtUET946mmLtdib6a1E0ZFkZTzsVlOJnf1EPVVm90bRnLafQdzor7gBL3Er3yjQcH8XePbIdp2e6FTR0rHg26cxAMw8w/aneFqhDuYpzDx5PY3z8mS/0G8/aNR51tszGhqtsyKxYktO3OZypbJmXYGEllP388Y+WUL3BtEznhmTZtremIcwyVbVOqLXPHc2/hiW29WNZa5x6jTtpF8UjQ9w6CYZj5Qc1G7rYt0CojdwAYTpowTBsRH6slEfFMqNozOKGqRe4t2sKlVW1OeQDXljEsjKSyi5e8C5lUY466cBCRUAAZ00badC5W8UjuNXoyWyZtWm6qpBL0rV0jrpArzz0eCeWkkTIMM7+oWXG3hMgR0+PjGWQsG+FQ/inHwgEEKOu5z1RtGQCojzpjiIQCOeWA376iVX52vi0D5DffSGqeuBJ31XQk7lmMNZkt89/u34KLvv0MLFu4Ns5wMuNmDmU9d47cGWY+U5PibtsCQjie+a/+7GIAwPHxNAzL9vXRiQiJSMhd5KTXn5kueuQOAB9822IsbIi6+erZCVUbI0kjuwLVI9BKfKMhx5bJmDYypoVoMH+lrcr2eWTLUbx+aDDntUff7AYAjKVM18YZnDCQzFgIENySDIlIiMWdYeYxNSnuqq5MkAhrFzcCAI6PG8iYwl2g4yUeDbqpkJkZnFA9Y1EjzlvejDM6nXHcfv25eOVv3uO+Hs2ZUDXcjJq0kSusKS2yDoecHPS07AWb8NoyloBp2fjLezfhIz980Xdcw0nDza4ZnHAi91g46N5dxCNBTKSL2zIDY2n8+b9uRJ9cZcswTHVQm+IuPedAgBCPhBALB7KRe4FofFlLHLt7xwBkI/foDETu7Q1RPPgXl6BTy5TR7ZkcWyZtuo0yvJG7KjVQF9Yjd0fcGzyLnjKmjW1H/UsYKIaTRtaWmTCQMe2cFbvxSNDNm5+MX208gt9t7cEdz71VdF+GYeaOmhR3tTpVZbu0xiMycvefUAWAC1a2YmvXMCYyppvnPhO2TDGULTMwloEQ2aYeebZMRp9QDeZky+iLnhpiIZi2jU3SjlEXCy+9IymkTRuhAGEoaTirXbU7lXi0tDx3Ff0fLaOeDcMws8e01IuIDhDRm0S0mYg2yG2tRPQUEe2RP1tmZqilY3k6KTXHIxiayMCQE5B+nLe8BaYtsKN7BBmZmTITtkwxVLTcJytDZsU9V1iV5x4LB5wJVct289wbPL1aDUtgOOlYKoVsKFXHZmVbwp1cjWptB+PhIDKWXTStckfPqPOze/I7BYZh5paZUK/LhRDnCCHWyee3AnhGCLEawDPy+Zzi2jLS/oiGA65QFRJsFeEOjhuu5z43kbtcgCTFXS28Shu5oqrSJOtjIUSDARha5K6fU2siklM3Z6yAb350yIm0l7XGATiRvG5DxWWOfbFJ1Z1S1EeSnDbJMNXEbKjXdQDulo/vBvChWfiMSfEuQlKLftJmYXFXFSNHUkbWlpmLyF0KqspcaU1IcffYMt1DKQQDhPb6aDZyN/PvRFoTUUfcZVrnSMpwm3+bWhTePey09lvSXAcAODaadid3gexK1fFJJlUHxtI4NppGgCbfj2GYuWe66iUAPElEG4noJrmtQwjRLR/3AOiY5mdMGZUtE1DiLvPCDcsuKNhK3IeThiusM7GIqRihYAChAKF/zBF3VRjMa8scHU6ioyGKUDDgVGw0/cVd2TIqchci255vRMujVx75omZnord/LJ1zrMVS9FUxM8WvNx7BilsfxXjaxE5pyaxb0YqkYeVcPBiGqSzTFfdLhRDnAbgGwM1E9C79ReEUSPFdLklENxHRBiLa0NfXN81h5KK6MCnPPeqKuyhotSjfejhpuHaHntUym8TCQTdyd20Zn8h9kRRcd4WqzxxCa33ELV+gUAui9AtGt2zKvUhm8QiRmx10RmcDgKztovjuE7sAOBPAe3odcT//JGdaZbyMUsMMw8wO0xJ3IUSX/HkMwG8AXACgl4gWAYD8eazAe+8QQqwTQqxrb2+fzjDyUIt4glKcw0Hdc/cX7FAwgPpoCMNJY9L9ZgMnVVNF7tlmHDrdw0lXiJ1sGQsZ00bUcyei6tHoq11VKQO95eAxmZfe2VjnbtPFvb0hitZExI3OvccybRvHxzMgAk6Svv1o2r/3K8Mwc0/Z4k5ECSJqUI8BvBfAVgAPA7hR7nYjgIemO8ipoiJ3ry2TmcRzBxxrxo3c52AyVRENBd2Jz4ZYGOEg5UTZQggcHU65/ngkGHAjc+841UVpJGlA1T07KqN0v+Ybi5uz+fe6505EOGlBHEcGkzn7K7snZdgYnDDQVBd2UzE5cmeY6mE6VSE7APxGWhchAL8UQjxORK8BeICIPgvgIICPTX+YU8NdoSp1LxIMwJCpg5OJe2NdGCNJA+FAYE7FPaalICaiIURlHrtiYDyDjGlrkXsAYzIyV+P8yafPx+6eUff8RlIGzl7ajM2Hh7CrZxTvXdvpm9bY0ZgVd+85x+Qdgh8p08JQ0kBLPOLeLYxx5M4wVUPZ4i6E2AfgbT7bBwC8J/8dc4c3FVKfUJ1s1WlTnWPLNMbCc5LjrtBXhtaFg4iGAh5/XE1+OpF7NBRwI2g1QXz12k5cvbYT97x8EICTmnhyez2Wt8Zda8W0svn/pu3MP8TCQdRHnbo63u8mGg5gYtw/CyZlWBiayKCpLuw2HtGtIIZhKktNrlDNLmJyTq9UW6YxFsZoyvSdqJxNYlpru2CAHHHXPPejMm1xcZMj7vp8gLc+vVqBO5oyUBcO4vTOBuzscSZFlS2j0i1VxK0mk/VFTM6xAjl3EJZWSjht2BiacAqdeXvQMgxTeWpa3F1bRka6tph81Wk07Nghhlk4ZXI2ULZMQmumrYuq8sxV2qJ+4fFG2+r80rKl4Kr2ehw6PiE7UTnHXCBXwSZkw5DGWFgey3OhkBdFhX43kTIsDE5k0My2DMNUJTUp7qq2jGvLBAMw7eL1YtyCXHMduUtRVWKbZ8sMpxAJBbBARtyRYFaEveMMaRelukgQK9viMCyBrsGkK+5tMt1Sfa63LLHC6/3rQp8yLQxNGGiOh92WgQcHJnDzL193680wDFM5arLNnt8KVcVkKY4RKaqZOY/cs92PACXuuZH74qaYm3evi7BXkPXCaHWRoNva742uIXfVqbpIqAVTyqbx3gVEPOPQH4+lLYylTcdzl+P+xSuHMJw00F4fxdc/uBbJjJVXa55hmLmhJiN30yvuk4ihjhLVyWrQzAbK63a971Awx3PvHk5hUVM2H10/B2+5X717lLJlAODzv9yUbQAuv5/T5EKlNllXJz9yD7hF1IDc3HsVnddHQwgGCPFIEGrN1/buEby4tx9n/O3j2HjweGlfAsMwM0pNiru35K8uWpOJtj7xWokJ1UZtYlO3ZY4OJV2/XY1T0VyX7RMLIKdefTwSRHtDFB85dwmAbCmBMxY5jUM+cPYiANlKlN4Sv947CH1MajFTVI69PhrC0ISz7dDABDYdHgIAPLz5aLHTnxWEEHhoc1fBwmkMU+vUpLi7toyfjTGZuMuVrOm5FnfX+1YTm1lRNS0bvSPZBUxAtok1ALQkwtDRbSdlu3z0/KUAgAP9Tpnf95+1CBu+eiUuXLUAANAuPfj+sdxuSqpAWfdwErc9ucttGAI43ZucsTvfk5pUVaj5jn3yM2eaPb2j+MHTuyHkhXwkZeDbv9vhzgu8+NYAbrlvM257cveMfN7eY2P4p2f2uJ/HMNVOTYq7bXsKh+meexFbRghnFWYlsmUadVtGitSx0TRsgRxbRl945I3cdZFVXvqSFue9qoZ7OBRwo3UA6JTH9pY8UN/HF+7dhNt/vzenH+vwhCdy1+yhlGm5Fwpv4bHJGE4auP6Ol3FooPh7rvr+c/jB03vcsg3/ubsfP1m/D292DQEA3uwaBpBtej5dvvyrLfjeU7vxVt/sXKwYZqapSXE3Pc06ciP3ySdUAad87VyXHwCAupwJVccC2XvMaf23oi3u7t/RmBVm74SlLvxK3Bc11YEI2N/viKZ3Uvny09px8+Un49ZrT8/Zrr4D1R9VtziGpC3jF7mPp01X3LuHUu7FthiPb+3GS/sG8H+e2TPpfimtv2zviPM5x8fVGJ3XtkhbaKYCbTXZvOEAzyEw84OaFPe8kr/B0j13wFlpOZeFw9R41YUnGs4uYlILkFSDbQBYqAm4Fz0iV+IekZG6EtxwIPc7CAUD+OurT8fChtzjqouOWg2rN8EeUraM5rkrDEvk1LPpGyuteba6BgR8vvqe4RQe2tyF0ZSBbq2lX++I81j1g1V15QdkCeXe0Zlp/6fWAmw6NDQjx2OY2aYmxd2ezHOfLM89lLsAaK4wPJ2fdFtmZ/coOhtjaElk7ZfGWOEM1qCmjK3ae3Txncya0lHjUV77MU0oVRs/P3EHnJx3dVG987l9JbXhMz1lI3T+/rEduOW+zbjn5YPo8RF3lb2jau6MSzump8Teri/u7ccbR4YAAE9u68FtT+7KsXTUhXFgvLQLFcNUmpoU97w891KzZbTXYnMp7m5zECXuWVvmwMA4VrUncvYvtc58XLNsojnfQWnvV9+HityVUAYDhOGkitylLeO54BwbTeOspU0AgJ8+vx/X3v6fRT9vWN4N+J3eW32OPbX32FjORUbZMipyV9aRyvxR4l+MT/z0FXzw/74AIQS+cJ8zx/BvG4+4r6u7FpURxDDVTk2Ke14q5BRtGSA7UTgXeHu2qmwZIYRTvyURyXvP335gDf7xj/LqtuWgXwRyxD1Q2q9d5d+rDBQlpE11YbcVoboIql6r+oXonGXN7mMhikfRSqC9fVuFENgvs27294+7x4mGAuhRkftEri2jIvehpFGy5w84Fw91p/KQTOMUQriRu/qcE4XfbDqC//KTlyo9DKYMalLcJ1vENFlVSP21WHjuvhrV0k79jIaDEMIRfaesbjjvPX986Uo3xdHLg3/xDvzwk+flbFP+eTBA7lxEMbwZQypi1m0hlcapJq8//vZl7mtt9VH87pZ34pozOwGgqDWjrBU1WavoG027gn+gfxy9I2kkIkEsa427dxDKYx+Toq72FyK7rRD6RPEjWxxBv/y0duzqGYUQTlcrZZMNJ0+syP1L92/BK/uPcwvFeUhNlx9Q3q1a1g+UHrnHQnMXuf/JpStxSns93nPGQgDZi0wqY2NoIpOX7liM85a35G1TUfhUJoq9dy8qWlf9ZvXjfuWaM/CBsxfnfIeNdSGcsagR3/zQmfjd1h7s7x/H5ZN8norchzzR8WHZMOSiVa14ed9xvNU3hoWNMdRHQ26ZYRVRj6VMCCEwkbHcSeSxlOlOiPqh31H8eP0+XHNmJ85d3oxnd/VhJGW63vuCRARDEwaEEHPWgrFaGE2ZvneQTPVSk5G715ZRBbmAIrVltIJcc1kTJRQM4Mo1Ha5gKFHtH3dy3Jt9Ivepoi4YUymrUCjXv1ETd3URbIqHcenqthyfX10EFiQiaIiG3Dz7Qqio2OtrK3E9WZZS2NUz6hwz5tShF0K4+e7jaRNp04ZlCzdltFid+WOaL5+xbFy6ug1Lmp3U067BpDtJu7SlDqYtTshVr1yrf/5Rk+Ku7iBVtkyinMh9Dm0ZL0qIe2VE2RyffsSkbJmpLM4qlFnU6BO5K/SsGSXuRISV7QnXN9dJZizcct8mdA1lRXQklSvuygNf2uIIbs9ICq0Jp9TwWMrEWNp07yrG0pY7mapy/kdTk1sp3nTJBYmIu/Crayjpirn6/BNxUtX7O2Gqn5q0ZdQEoBKneNQ/a8RLtEK2TKFxKNHx89zLPWZoKraMz3cVDpJb/oAofx/9u9btm87GmO9q1UfeOIqHNh9FOBhwRVRfpARks3WWtWZX6bYmIrCFwGjKdKN2wInc1WRqqZH7sEesWxNRt9zD0aGkmxarBP/uFw/gt29044Vbr8hJPa019LaMLO7zj5qM3FUaYVTLG1eUHrlXUtydz+4ZdjI0ZsSWCU/dlon7WFORYMD9bqKhQJ733JbILqLSxb05HvaNeNUkamMs7Ga6pAw7p4ZLSkbiy1uzq3SdyD2MsXRW3IMBwljadCdTVeReTJhSZu5kYWsi7H7nw0nDbUKietj+9Pn96BlJ4WARm2m+c0xbtDaSZFtmvlGT4q5u4/0EerIFPFUj7lKIVY52pWyZhGaxqLuHaDjoWlZ+35GeiaNPYjbVhTGUzE8jPC4nQpvqwhjPZGv6vLRvANuPOtk1KnLX6+u0JiKol557v8yU6WyMIWlYeeJezCP33im0JqIIBwOIBAOYyFhuSYMOz8rgnT2j6BlO4antvZMef75yULPRillbTPVRo+KeG7nrFKsKqagKz12Je13lI3dV1qAuHHRFvdB3+dX3n4FYOJDjzTfHI0gZdp6QHh9Tue0yI0VWqPzEna/g2tv/E0IIV9z1ifFWOUkLAEcGHbtncXMMKcPChBTzTtdzLybuuZG7uuOIR4OYyJjuHYVX3Hd0j+DvH9uBz92zIa9cci2wX7szGeEJ1XlHbYq7aSESCvjmc0+WLROtlshdRtlK3JtmQtzlMWNTyALSU0iV6DbHw1lxL3AX9CfvXIWd37wmx49W5zDiyRPvlbf+agWo+hzFju5R94IQCwWxsi3hHk81KtnZPQrAWSeQ0iL31kQEwQAVjTrTppXT9MTNsoqEZOSe6+ErXtjbjye398AWU6t+OV/Y3zfu/n/hyH3+UZPinjZst1qhl8nyk3X7o7LiriL3NBpioZy+qNM9ZkO09Dl0XZwX+ETuehZSMZS4excoqZx2VVxML3wGAIeOjzslmOXF+oHPXYw/vmQlLly1wC15cP+Gw7hoVSva6qNIZiz3M5rqwjm58IVIGTaioSB++ScX4lsfPtPdXhdxIvextIlQgHJq9QDA64eG3KjfLxNoPmPbAhsPDWJVWz0SkWDNeO4pw3LvxGqd2hR30ypLnPVIdC4Lh3mJaZ57ywz47UBW3HVrYyq0SWGLaZ57fArH0icodVT6o4rcveKeNCykMpb7+2hviOJv/2AN6qOhHE//v75jJerCQaRM2y3/21rv5MIXE/e0YSEWDuAdp7Thkxee5G5PRIIYTztiUB8LoS4cdCtWqhhBnVexHP75xn/u7cemQ0P41MUnobEuXDOR+3u//xzWfu2JSg9jTqhJcU8Zdl7+9VSppOeuLkymLWYkUwbQxb287FcVuUdDATdN1C+bphBu5O7JmFGWRyFbZiJjIWlYvhfbkxfWu49XtiVQFwnCsgV6R9IIBwkN0RAaYuHikXuBYCAeCSEpbZlEJAQicr+/j563FD//zNvx8M2XoiUerjlbZr8s1HbNmZ1oiIVqJhVS/Z5OhI5aNSru1rTz1Ctpy+jR60xkygDZkgxTsVJ01OKkaDjgXjjjUziWKqHgLS3g1l+X6Yx6KiXgLHJKGrbviuHFTdkJzpMWxN0L2NGhJFriERCRjNyLpEIatu/FPB4JYjxjYnA8416c1PfQkojgstMWYvmCOBbUR92UTj9efKsftxdpQFJtdI+kEAkG0BqPoLGEC+R84/gkv69aoSYXMaWM/Ejsjk+fj+0l1BT/9Z+/A/++qWvSxU6zTSwcdFZfps0ZyZQBgPGMyjgp71eu7IhoKOguEptS5O5jy9i2cMelaGvIj9xThuX7+9DnT2LhoHsBODqUdP3xxlgIR4ecienndvfh+0/vxj2fvTBnJW2hYCAedSL3rqEkTlrgTOT6zV20JiLuxcmPz92zEaMpEzu6R3DGokZ84T2rC+47l3hr5KholojQPZRCZ1MMgYBzgVTpprXCgYFx9260VqnRyD0/Envv2k588cpTi773/JNa8M0PnVnxwlBt0p6YidWpQDZCTpRZM2eRXLF5ysJ6V5CnErk3REMgyhX3/T4+tUo3JHIuKErcC9X6uf+mi/DjT50PIDtP0jWUcsW9IRbGaNrAsdEUbvjZq9h0aMhtXahIm/42XiISxFjaRNdg0l2xqtIym7Tfy4JEZNLIXS1++t3WHtz21O6qsAS+dP9mnPfNp3K2vfN/P4u/vHcTAKeYWqccd0MsXDO2jOJAf23ZaH7UpLiXO6FaTajWeyr1b7qcschp03fO8uay3v/eNR2484Z1+LN3n4yT5ZguWtVa8vsDAXIWMknPXQiB93xvfe4+lBX3BYko6qMhJDMmkhl/zx0ALly1AO+TJYXVPv1jaVfcVbbMD57O2iLeBh6FIve6SBDHRtMYz1iuuKua9hevWuDu15KITHqbH/LUzz8iq1xWkt9s6sLghOFelCxb4MhgEr99oxtp00L3SNK1vRrrik9KVyuWLfDU9t68C+ps2zKD4xm8uLd/Vj+jGDUp7iq1bT7TP+r88SlRni4fOHsRnvvry/HO1e1lvZ+IcNWaDgQDhHec0obn/vpyXHfOkikdo6ku7EbuSiSBbMplSzziWj3tDVFnQtMoPKHqRb+gq36wDbEQhiYMPLLlKN65ug1AbhVIwN/GA3LnJ1RdmQ+dsxjN8TBWdzS4ry1IRDA4kSnYFKTf00N229HhoucyV7wqG37rF7wth4dl5O6cc0MsjJGkURV3HFPl7hcP4E//ZQN++0Z3zvhnu+nKh3/4Aj7x01fcxW3HxzP4wdO753SxW22Ku2lVNNtlJlBli0/vnBlxJyIsXxAvvqOHb3xwrSuKOuUcq7ku7Oag63nh7dL7bE1E3Ah6YUMU8UgQExkLIykjr42fH7pAn9bpZNI0yHTJ0ZSJj61bhmCA3O5NikLZVboVpDpM/eDj5+L1r16Vs59TxCxrOd3z8kH868sHATjzCl4/Xm80rrPl8BD++79tcSe/Zwu98cbuHmcBWNdQ9m7ijSNDMCyBxc0yco+FYdoibyXvZBwfz+DmX7yed2Gba1SK6tGhpGupAfnrLaZK32gaf/GLjb7nlzYtHBiYyPn8+147hB88vQffeGTbtD53KsxvBSxAugYi91/+6YX4whWn5Hi7leDGd6zAPZ+9cEaO1RSPuH1S9bzwU2RKYzScLUq2sCEqFxFZ6B1Ju6UEJkMXY3XHo688XdmWwMKGaM5dA1DYxjtNi85PXZh97F35rCwg1Tz7f/77Vnz137cCcCJEyxb4zCUrcP0FywEAfQUmJ2/851fxwIYjs74gSr+4qcddmlW04cAggGz5BvUdTsV3f3p7Lx59sxsvvTXg+7oQAl1DyWldyNKmlVO50g+1unlgPIPxtCbu04zcv/vETjz2Zo/buUvn4EDWzz8gf5dbDg8BAF7df3xanzsVZk3cieh9RLSLiPYS0a2z9Tl+pIz5H7mff1Ir/tt7T6v0MGYU3ZbRBWztYkeI+0czTh59OIAlLXWIR4I4OpRExrSxsBRx1wR6tRRjvXTDkuY6dDTG0D2c63k7Nl7+38u7TnUsrI7G6KStCZUff3gwmbf6UXWRWndSK779kbOwIBEpGM2q+YhdMpoWQmBr18xbOLrnry50qj5PPBLEhoOOAKm2j+o7nIqV8YoUMf2OQOeu5/fjkn/4Pb792I4pjj7LJ+98BVfetj6vBeCRwQn370wJ7f7+8ZzfTbk1+QfHMzjQP44th53fy1Gf89MvlPsHxiGEcC+Yh45PzFnLwllRQCIKAvh/AK4BsAbA9US0ZjY+y4+0ac/7CdVaRLdldkoBA7Ii2juaQiBA+Lc/ewf++NKVqItkuzeVErnrAq2i+DWLs7ZWczyMUxbWY3evN1vGP3KPhAJ48kvvwkM3Xzrp566QE8x//+gO/O/Hd7rbR1IGHn3jKEIBwsUnOxOwbfVR9PvYMrpVs7PHSdn9981d+MA/PY/H3uye9POnyu5e57s/raPB9dp39Y5haUsdTllYn62yKSdUlSXlzTIaTRn4zaYjvl78a9LL9xM/AFi/uw8AsPHQYFnnYFo2NhwcxMGBCTy945i7/fDxCVz6nWdxzQ+eQ8qw3DHv6R116/wDwGAZ4p42LZz/v57CZf/4H9grF3nt6B7N20+/oO3pHUPfaBoD4xmsXdwIU05czwWzFd5eAGCvEGKfECID4D4A183SZ+VwVHbO8S5jZyrP4uY6DE0Y6BpKYkf3CD5y3hJs+OqVeMfJCxAKEG593+kAgDOXNKExFkY8HHR93s6m4r/PhY1RhAKE7370bHfbKi3biIhwemcD+kbTbvR8cGAchiUK/r2c2tHgilwhFkhbZs+xMdz90kF3+3O7+3DPywdx9dpO17ppa8hG7qZl47tP7MS3H9uBF9/KZla8ILMsNh50hO+hzV1Fz30q7OgeQXM8jLOXNrnivqN7BKd3NrqtDBORIFrlArpTFtYjGCC3QJviaw9vw5fu34IXPdZLz3DKXQna5SNkadPC6/LcdvWMFpyIngz9zu+V/dnPv+O5fQCAo8MpnP4/H8fAeAar2hM4MDDhRvGdjTHs6B7By/v8LaNCPL7VKRIHwG3j+NK+AWzyXKC6hpIIBwlXr+3AaweOu+trVKP4t/pyL5KzxWwtYloC4LD2/AiAmTFuNdbv7sP/+u32nG1qOfv7z1o00x/HTJP3n7UI33l8Jz76oxfRN5rGmkWNrqju/ftr8/bXF0mp7JfJiEdCecchItx6zenu8zXSi//wD19ALBTEaMoEEXDtWZ1lnZP6DMXWb1yNXT2j+MMfvYgvP7AFiWgIX/uD7E1rW30Uv3uzB1fdth4p08Lh40k59iDqwkH8xWUn43tP7caVt613o94ntvXiytvWY6ZWXnQNJfG2pc3obIrh2GgaV922Hm/1jeHaMztx1tJm/GZTF9YuaXKtqGgoiJPbE7j7pQN4YluPexwVvd5y36acGkiqKueiphie39uPq27LTXmdyFgYz1i47pzFeGjzUVz5/fVuS8xSUZ/REg/j/tcO4/k9zgXx4PEJvP/sRXj0jezdzl9ecQq+dP8Wdx5EOWwfv+NlrNZKWBTjmOeO68efOh+f/+Um3PCzV3PuLHtHnDUCF61agCe29eKv/+0NAMCHz1uKu57fj7/61ZacYOK/vH0Z/uSdq6Zw9qVRsRWqRHQTgJsAYPny5WUdoz4awuqO/F/OJzoby8rmYGaX5Qvi+Kv3nort3SO4MBjANUUuwB9dtxRp00ZHY8z1tcvhz959svv4vJNacP0Fy3IWU521pDmnEUg5/PhT5yNlWKiPhnDWkiZ84sLlGE4a+ONLVuTMF1x/wXKYloCAEwLeePEKREIBvLxvABesaMV15yzB/oFxpAwLp3bU4yPnLsWLbw2gZ2TmbuVXd9Tjj85fhiUtdTgwMAHLtnHGokZcd+4SLG+N43PvWoVPXJj7f/LzV6zG41tz7aFTOxvQGAvlFYMDgKvWdOCy09rxwIbDea8BwJ9fdjLed2YngkRImeWlB161pgOXn74Q9792yN12WmcDbr78FHzk3CV47M0eLGqK4Q/OXowth4dxbDSFhmgYH3v7Utz76mHYQuT1F5iM1R31+ODbFmPToSGYtsDbljbjzhvW4cfr34Jp2zn7vfvUdlx++kK8eWQYKdPCKQsbsKS5DnfesA7//OKBHCtrtlwGmo3cVSK6GMDXhRBXy+dfAQAhxLf99l+3bp3YsGHDjI+DYRimliGijUKIdX6vzZbn/hqA1US0kogiAD4O4OFZ+iyGYRjGw6zYMkIIk4g+D+AJAEEAPxNCzF32PsMwzAnOrHnuQojHADw2W8dnGIZhCjO/V/owDMMwvrC4MwzD1CAs7gzDMDUIizvDMEwNwuLOMAxTg8zKIqYpD4KoD8DBojv60wagsi1P5hY+39rlRDpX4MQ639k615OEEL4deKpC3KcDEW0otEKrFuHzrV1OpHMFTqzzrcS5si3DMAxTg7C4MwzD1CC1IO53VHoAcwyfb+1yIp0rcGKd75yf67z33BmGYZh8aiFyZxiGYTzMa3GvZBPu2YKIfkZEx4hoq7atlYieIqI98meL3E5EdLs8/zeI6LzKjXzqENEyInqWiLYT0TYiukVur7nzJaIYEb1KRFvkuX5Dbl9JRK/Ic7pflsgGEUXl873y9RUVPYEyIaIgEW0iot/K5zV7vkR0gIjeJKLNRLRBbqvY3/K8FfdKN+GeRX4O4H2ebbcCeEYIsRrAM/I54Jz7avnvJgA/mqMxzhQmgC8LIdYAuAjAzfJ3WIvnmwZwhRDibQDOAfA+IroIwHcAfF8IcQqAQQCflft/FsCg3P59ud985BYAO7TntX6+lwshztHSHiv3tyyEmJf/AFwM4Ant+VcAfKXS45qhc1sBYKv2fBeARfLxIgC75OOfALjeb7/5+A/AQwCuqvXzBRAH8DqcvsL9AEJyu/s3DacXwsXycUjuR5Ue+xTPcykcQbsCwG8BUI2f7wEAbZ5tFftbnreRO/ybcC+p0Fhmmw4hhGpg2QOgQz6ume9A3oafC+AV1Oj5SotiM4BjAJ4C8BaAISGEKXfRz8c9V/n6MIAFczrg6fMDAP8dgGowugC1fb4CwJNEtFH2iAYq+LdcsQbZTHkIIQQR1VSKExHVA/g1gC8KIUaIyH2tls5XCGEBOIeImgH8BsDplR3R7EFEHwBwTAixkYguq/Bw5opLhRBdRLQQwFNEtFN/ca7/ludz5N4FYJn2fKncVov0EtEiAJA/j8nt8/47IKIwHGH/hRDiQbm5Zs8XAIQQQwCehWNLNBORCrL083HPVb7eBGBgbkc6LS4B8EEiOgDgPjjWzP9B7Z4vhBBd8ucxOBfvC1DBv+X5LO4nUhPuhwHcKB/fCMebVttvkDPvFwEY1m4Bqx5yQvS7AOwQQtymvVRz50tE7TJiBxHVwZlb2AFH5D8qd/Oeq/oOPgrg90Kas/MBIcRXhBBLhRAr4Pzf/L0Q4pOo0fMlogQRNajHAN4LYCsq+bdc6UmIaU5gXAtgNxzv8n9UejwzdE73AugGYMDx4T4Lx3t8BsAeAE8DaJX7EpyMobcAvAlgXaXHP8VzvRSOT/kGgM3y37W1eL4AzgawSZ7rVgB/K7evAvAqgL0AfgUgKrfH5PO98vVVlT6HaZz7ZQB+W8vnK89ri/y3TelRJf+WeYUqwzBMDTKfbRmGYRimACzuDMMwNQiLO8MwTA3C4s4wDFODsLgzDMPUICzuDMMwNQiLO8MwTA3C4s4wDFOD/H9K8bmU/IkfpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BMode Image (expected)\n",
    "arr = itk.array_view_from_image(bmode_image)\n",
    "plt.plot(arr[0,:,0])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
